Correlation between music APP listening habits and depression tendency in college students based on SMOTEENN algorithm

Objective To investigate the influencing factors for tendency towards depression in college students having music listening habits with music APP, and develop a prediction model and further optimize it. Methods A total of 1 157 college students were subjected with convenient sampling and surveyed with questionaires between April and May 2023. Univariate analysis and logistic regression analysis were employed to identify the influencing factors. Then a prediction model was constructed based on these factors. SMOTEENN over-sampling algorithm was utilized to enhance the dataset and construct the prediction model. Results Logistic regression analysis revealed that female (OR=1.730, 95%CI: 1.257~2.396), senior grade (OR=2.649, 95%CI: 1.198~7.506), postgraduate grade (OR=2.041, 95%CI: 1.231~ 3.885), major in Science(OR=1.573, 95%CI: 1.052~2.350), listening for a duration of 0.5~2 h (OR=1.661, 95%CI: 1.011~2.695), music style of melancholy (OR=2.668, 95%CI: 1.701~4.226) and of nostalgia (OR=1.751, 95%CI: 1.086~2.837), and frequency of comments on 0~5% of songs (OR=2.938, 95%CI: 1.018~8.417) were independent risk factors for depressive tendency. Time since listening to music for 1~3 years (OR=0.547, 95%CI: 0.347~0.872), listening to music from 14:00 to 18:00 (OR= 0.375, 95%CI: 0.167~0.845) and 18:00 to 21:00 (OR=0.313, 95%CI: 0.148~0.671), and preference for Chinese style songs (OR=0.711, 95%CI: 0.541~0.941) were independent protective factors. The logistic early warning model based on SMOTEENN algorithm demonstrated optimal predictive performance with an AUC value of 0.923. Conclusion Our constructed logistic regression model has identified 9 independent influencing factors associated with depression tendency among college students.The early warning model based on SMOTEENN algorithm can predict the depression tendency more accurately for college students.